How to Lie with Statistics

The point is that when there are many reasonable explanations you are hardly entitled to pick one that suits your taste and insist on it. But many people do.

The importance of using a small group is this: With a large group any difference produced by chance is likely to be a small one and unworthy of big type. A two-peracent-improvement claim is not going to sell much tooth-paste.

So when you see an average-pay figure, first ask: Average of what? Who’s included?

What’s Missing?

So it is with much that you read and hear. Averages and relationships and trends and graphs are not always what they seem. There may be more in them than meets the eye, and there may be a good deal less. The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify. Stati

This book is a sort of primer in ways to use statistics to deceive. It may seem altogether too much like a manual for swindlers.

Not all the statistical information that you may come upon can be tested with the sureness of chemical analysis or of what goes on in an assayer’s laboratory. But you can prod the stuff with five simple questions, and by finding the answers avoid learning a remarkable lot that isn’t so. Who Says So?

A river cannot, we are told, rise above its source. Well, it can seem to if there is a pumping station concealed somewhere about. It is equally true that the result of a sampling study is no better than the sample it is based on. By the time the data have been filtered through layers of statistical manipulation and reduced to a decimal-pointed aver

Percentages offer a fertile field for confusion. And like the ever-impressive decimal they can lend an aura of precision to the inexact.